In farming regions that receive limited moisture, fallow phases are used to maximize crop yield potential with special attention given to weed removal. When fields are laying fallow, it is very important to remove the weeds that are trying to establish themselves. When the density of the weeds is low, it is not efficient to spray the whole field with herbicide, so proprietary sensor-based spray booms are used to identify and spray herbicide directly onto the weeds. Researchers from the University of Sydney have developed an open-source, low-cost and image-based device for weed detection in an effort to give this technology wider availability. The demonstration system uses a Raspberry Pi 4 with a Raspberry Pi HQ Camera and 6-mm focal length lens. The Python code and detailed instructions reside in the OWL github repository.
OWL represents a novel opportunity for community-driven development of weed recognition capability using existing ‘off-the-shelf’ hardware and simple yet effective image-based algorithms. The combination of the OWL device, supporting documentation and repository create a channel for practical education of key image-based weed detection and actuation concepts for growers and the wider weed control community. The topic is of particular importance now, given the emergence of image-based in-crop weed recognition technologies. OWL has been designed as a community-focused educational platform that will grow over time with initial baseline validation performed in the present research.
Guy Coleman, William Salter, and Michael Walsh. OpenWeedLocator (OWL): an open-source, low-cost device for fallow weed detection. [open] Sci Rep 12, 170 (2022). (DOI: 10.1038/s41598-021-03858-9)
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